A Novel High-Throughput Molecular Counting Method with Single Base-Pair Resolution Enables Accurate Single-Gene NIPT

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bioRxiv preprint doi: https://doi.org/10.1101/597732; this version posted April 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

A novel high-throughput molecular counting method with single base-pair resolution enables accurate single-gene NIPT

David S. Tsao1,*, Sukrit Silas2, Brian P. Landry1, Nelda Itzep3, Amy B. Nguyen1, Celeste K. Kanne3, Vivien A. Sheehan3, Rani Sharma4, Rahul Shukla4, Prem N. Arora4, and Oguzhan Atay1

1BillionToOne Inc., Menlo Park, CA 94025, USA 2Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA 3Department of Pediatrics, Division of Hematology/Oncology, Baylor College of Medicine, Houston, TX 77030, United States 4Yashoda Super Speciality Hospitals, H-1 Kaushambi, Ghaziabad, Uttar Pradesh 201001, India *[email protected]

ABSTRACT

Next-generation DNA sequencing is currently limited by an inability to count the number of input DNA molecules. Molecular counting is particularly needed when accurate quantification is required for diagnostic purposes, such as in single-gene non-invasive prenatal testing (sgNIPT) and liquid biopsy. We developed Quantitative Counting Template (QCT) molecular counting for reconstructing the number of input DNA molecules using sequencing data. We then used QCT molecular counting to develop sgNIPT of sickle cell disease, cystic fibrosis, spinal muscular atrophy, alpha-thalassemia, and beta-thalassemia. Incorporating molecular count information into a statistical model of disease likelihood led to analytical sensitivity and specificity of >98% and >99%, respectively. Validation of sgNIPT was further performed with maternal blood samples collected during pregnancy, and sgNIPT was 100% concordant with newborn follow-up.

Introduction

Next-generation sequencing (NGS) has revolutionized both research and clinical practice. In 2001, a global effort was required to sequence the first human genome; nowadays over 100,000 human genomes can be sequenced in a single center.1 In many biological subfields, significant advances have been made by innovative experimental designs for generating data resolvable by NGS.2, 3 In the clinic, performing whole exome sequencing is now considered the standard of care for any undiagnosed congenital and neurodevelopmental disorders.4 Translation of genome sequencing technology to the clinic has been even more widely adopted in prenatal and oncology care. Now that targeted oncologic therapies are indicated only when specific genetic profiles are found in the tumor, DNA sequencing of biopsied tissue is FDA-approved and covered by national insurance programs.5, 6 More recently, it has been discovered that circulating tumor DNA (ctDNA) can be found in the cell-free DNA (cfDNA) purified from plasma.7 This has led to “liquid biopsies” that detect cancer mutations by DNA sequencing of circulating cell-free DNA (cfDNA) purified from a blood sample.8 In prenatal care, cfDNA of fetal origin is used to detect fetal aneuploidies as early as the 10th week of gestation from maternal blood.9, 10 These non-invasive prenatal tests (NIPT) are routinely used in clinical care, and they are offered to all pregnancies via national health insurance.11 However, these cfDNA-based tests have been limited to detection of chromosomal abnormalities or large structural variants in prenatal testing10 and late-stage cancers in liquid biopsy.12 Yet, several prevalent genetic disorders (e.g. hemoglobinopathies, cystic fibrosis, and spinal muscular atrophy) are caused by single nucleotide variants (SNV) or a single gene copy number change. These recessively inherited disorders are particularly challenging to detect because they require precise quantification of bioRxiv preprint doi: https://doi.org/10.1101/597732; this version posted April 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

fetal cfDNA over a significant background of identical maternal cfDNA. In liquid biopsy, even though copy number variation is a hallmark of cancer,13 current FDA-approved diagnostics are limited to resolving a 30% to 120% increase in the copy number found in cfDNA and therefore are only applicable to late-stage cancers.14–16 These limitations are largely due to traditional library preparation methods for NGS that introduce significant levels of noise that obscure the relationship between DNA molecular counts of the input sample and the final sequencing read count. Without the ability to perform absolute quantification, NGS cannot reliably quantify fetal SNVs in prenatal testing, detect gene amplifications that are present in Stage 1-2 cancers, or sensitively detect rare cell-free tumor DNA sequences that may only be present at 1-10 molecules in the sample.17 Importantly, increased read depth does not increase sensitivity in these contexts. Amplification and/or enrichment biases can result in 10-fold variability in output read-counts across loci, while accurate determination of single-gene NIPT or early stage liquid biopsy may require quantification of input DNA molecular counts at differences <5%. In the vast majority of cases, the major sources of noise are introduced during amplification and library preparation, and therefore increased sequencing depth does not result in improved accuracy.18 Currently, the only method to obtain accurate absolute quantification of DNA molecules is via digital PCR (dPCR).19, 20 While dPCR has been a revolutionary technology, especially for developing reference materials, its limited multiplexability of 2-4 variants at a time significantly limits its clinical use. Moreover, since dPCR is not sequencing-based, it is unable to resolve between multiple, adjacent variants as is commonly the case in single gene disorders and oncogenes. We developed a sequencing-based molecular counter with single base-pair resolution and vastly superior multiplexing ability. Since molecular abundance information in sequencing read depth data is typically corrupted by library preparation, we developed a technique to encode molecular abundances prior to PCR amplification and subsequent library preparation steps using Quantitative Counting Template (QCT) molecules. Absolute quantification of DNA molecules is then decoded via customized bioinformatic analyses. We applied our molecular counter technology to single-gene NIPT for hemoglobinopathies, cystic fibrosis, and spinal muscular atrophy. These recessively inherited disorders require high-accuracy molecular count information to be able to determine whether a particular mutation is elevated relative to the maternal background. Medical guidelines recommend that all pregnancies should be screened for these disorders.21, 22 Hemoglobinopathies, i.e., sickle cell disease, beta-thalassemia, and alpha-thalssemia, are the most common genetic disorders in the world, affecting more than 300,000 births each year.23 The number of pregnancies that are at-risk is even higher, with 7% of pregnant women globally carrying a significant pathogenic variant for hemoglobinopathies alone.23 In the US, 7% of African-Americans24 and 1.5% of all newborns are carriers for sickle cell disease (sickle cell trait, SCT).25 The carrier rates for cystic fibrosis and spinal muscular atrophy are 3% and 2%, respectively.26, 27 Although NIPT for chromosomal abnormalities is widely used, NIPT for recessively inherited single gene disorders are not currently available. Rather, the only way to diagnose these disorders in the fetus is through invasive methods such as amniocentesis or CVS, which carry significant miscarriage risk.28 In this study we showed that our QCT method accurately counts molecules with single base-pair resolution. We then applied QCT molecular counting to NIPT for mendelian disorders, including sickle cell disease, thalassemias, cystic fibrosis, and spinal muscular atrophy. These NIPTs require only a single 10mL sample of maternal blood (Fig. 1). We validated our NIPT with preclinical samples (estimated sensitivity of >98%+ and specificity of >99%) comprised of genomic DNA sheared to mimic the fragmentation of cfDNA. Crucially, reliable NIPT results were only obtainable via accurate molecular counting of these genes. NIPT assays were also performed on non-pregnant cell-free DNA control samples to show the concordance of sheared DNA samples with cfDNA. Finally, we performed NIPT on maternal blood samples from 37 pregnancies. All NIPT results were 100% concordant with the newborn genotype, even in a challenging sample with fetal fraction as low as 5%.

Results

High throughput molecular counting with single base pair resolution We ﬁrst developed a technique to count the number of DNA molecules in a PCR using amplicon NGS workﬂows (Fig. 2). In this assay, a number of Quantitative Counting Template (QCT) synthesized DNA molecules are spiked-in to the cell-free DNA

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specimen prior to PCR amplification. The QCT sequence is designed to co-amplify at the same rate with its corresponding gene of interest by incorporating homology regions, especially in PCR priming sites (Fig. 2A). QCTs are designed such that the number of molecules added can be independently calculated from sequencing data. The relationship between read depth and molecular counts is then used to also determine gene of interest molecular count in the input sample (Fig. 2B). To facilitate counting of the number of QCT molecules spiked-in to each tube, the QCT sequence contains a barcode comprised of 10 randomized bases in which A, C, T, or G is stochastically incorporated during oligo synthesis (Fig. 2A). Because up to 410 QCT sequences are synthesized in a pool and only ∼100 to 1000 QCT molecules are spiked-in to a PCR amplification, it is exceedingly unlikely for any two QCT molecules to have the same sequence. The randomized sequence of each molecule thus comprises an Embedded Molecular Index (EMI) that identifies the molecule and its amplification progeny (Fig. 2B). After PCR amplification and DNA sequencing, the number of QCT molecules added to each specimen should correspond to the number of EMI sequence clusters observed, and the gene of interest molecular count is determined . To test our ability to count the number of QCT molecules present in a reaction, we added 4x, 2x, 1x, or 0x QCT molecules to PCRs, with 1x corresponding to approximately 100 QCT molecules. Twenty-four replicates were assayed at each spike-in level, for a total of 96 reactions. Embedded molecular indexes from the same PCR tube that differ by up to 2 mismatches were clustered together in order to avoid counting spurious indexes that arise due to sequencing error. We observed the expected 1 relationship in index clusters detected by sequencing and the 2 dilution series of QCT molecules (Fig. 2C). The number of QCT molecules added at each level is expected to vary among replicates due to sampling noise; addition of N molecules to a √ reaction should exhibit a standard deviation N according to Poisson statistics. This effect was observed in our data, as the variance of QCT molecule counts increased with more molecules added (Fig. 2C). We confirmed that the EMI cluster numbers were robust to read-depth variation by repeating cluster analysis on sequencing 1 data that had been subsampled to 2 of its original read depth (Fig. S1). The number of EMI clusters obtained from subsampled data matched nearly perfectly with the full sequencing data in the vast majority of cases. In several PCR assays, subsampling the reads resulted in an underestimate of EMI clusters compared to the full read data. This effect did not occur in assays with >10 reads per EMI cluster on average, suggesting that a sequencing depth of at least 10 reads per molecule is sufficient for robust results. Quantification of assayable human genomic equivalents (number of haploid genome copies in the sample) relies on the assumption that the gene of interest and QCT DNA co-amplify at exactly the same rate. Therefore, QCT sequences are designed to use the same PCR primer binding sites and generate the same length amplicon as the gene. However, since the internal sequence differs between the QCT and gene, QCT molecules could hypothetically be amplified at a different rate compared to the gene. To ensure that the PCR amplification is robust to sequence composition, we examined the average depth per molecule, hDQCT i, of two QCT pools. Pools QCT1 and QCT2 were synthesized with QCT IDs TCGCC and CTAGT, respectively. Approximately 100-200 molecules of each QCT pool were added per PCR, and the average read depth per molecule from each pool was compared across 48 PCRs (Fig. 2D). Since QCT pools incorporate 10 randomized bases in addition to the 5 mismatches in the QCT IDs, the sequences of the ∼100 QCT molecules from each pool in a PCR assay are highly diverse.

In each PCR assay, hDQCT1i and hDQCT2i were highly consistent. hDQCT1i spanned an 8-fold range across PCR assays due to endpoint PCR variance and uneven normalization prior to sequencing. Nonetheless, hDQCT2i was highly correlated with 2 hDQCT1i with R > 0.99, and the mean difference between hDQCT1i and hDQCT2i was only 0.0058% (Fig. S1B). Since the sequence differences between the two QCT pools is much higher than the QCT and gene sequence differences, we expect ∼0.006% to be an upper bound on the ampliﬁcation difference between QCTs and the gene. Introducing distinct sets of QCT pools into all reactions provides an internally controlled upper bound for the error of hDQCT i measurements, which can be important for detecting assay degradation due to low sample quality (e.g. ethanol or salt carryover from DNA extraction). We then sought to quantify the amount of human DNA present in a sample using QCTs. The number of DNA molecules DG corresponding to the gene of interest was calculated as GE = ; where GE is the haploid genome equivalents, and DG is hDQCT i the read depth of the gene (Fig. 2B). To more closely mimic the fragmentation pattern of cfDNA, human DNA was acoustically

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sheared to a mean fragment length of ∼150bp. A dilution series of 2-36ng sheared DNA was prepared with approximately 200 QCT molecules per reaction (Fig. S1C). PCR primers targeting a 150bp region of HBB exon 1 were used to co-amplify the QCT and sheared DNA molecules to yield a 150bp amplicon. Since not all sheared DNA molecules span both primer binding sequences, only a fraction of the sheared DNA would be amplifiable by PCR. After counting the number of amplifiable sheared DNA molecules, regression analysis showed that 64 GE/ng of sheared DNA was detectable, compared to the theoretical maximum of 278 GE/ng implied by a haploid genome mass of 3.6pg (Fig. S1C). This demonstrates the importance of directly measuring assayable genomic equivalents as opposed to DNA mass alone, since cfDNA is also fragmented with mean length ∼167bp.29 We then established the precision of GE estimates by measuring a smaller range of 30-33ng of sheared DNA with QCT molecules. Again, we reproducibly obtained genomic equivalents of ∼60GE/ng sheared DNA (Fig. 2E). Measured GE increased linearly with more DNA mass, increasing from 1850GE at 30ng to 2070GE at 33ng. The coefficient of variation (CV) of HBB molecular counts was 4.8%. Since the overall CV is affected by pipetting precision and Poisson sampling of limited DNA molecules (2.2% CV at 2000 molecules), 4.8% CV is the upper bound for QCT molecular counting precision.

Ultra-rare variant calls enabled by QCTs A particular challenge in detecting rare variants is false-positives due to contamination from another positive sample. This becomes particularly acute when multiple libraries are processed in parallel and sequenced in the same run.30, 31 Since the sequence diversity of QCT molecules aliquoted into each reaction is very small compared to the total QCT diversity in each synthesized pool, we realized that the set of EMI cluster sequences associated with each PCR can be used as a fingerprint to identify the sample. Given a total pool diversity of 1 million sequences, ∼10443 fingerprints are possible if 100 sequences are added to each PCR. Analysis of PCR fingerprints across the entire sequencing workflow can then be used to rule out the possibility that detected rare variants in any given reaction are from sample cross-contamination (Fig. 3). We further analyzed the experiment performed in Fig. 2C to quantify contamination using QCT fingerprints. The sequencing depth for each EMI cluster in a PCR library was distinctly bimodal, with most EMI sequence clusters read at depth >30x. A minority of EMI sequencing reads were also present at depth 1-2x (Fig. S2). EMI sequencing clusters were classified as high-depth or low-depth (see methods). Low-depth EMI sequences could arise from (i) sequencing error, (ii) errors introduced during PCR amplification, (iii) cross-contamination during sample handling, or (iv) index misassignment.30 A low-depth EMI sequence cluster (typically 1-2x) observed in a PCR was classified as a contaminant if it was also observed at high-depth in a different PCR. We then computed the contamination fraction for each PCR as the number of contaminated QCT reads over total QCT reads (Fig. 3A). The PCRs with 0x QCT molecules should therefore register 100% contamination. QCT contamination analysis measured >90% contamination in 0x QCT wells, suggesting that this method is highly sensitive for detecting contamination. The remaining ∼10% of undetected contamination could be due to sequencing error or contamination that had occurred prior to PCR amplification. Unexpectedly, we found that PCR libraries barcoded with D701 and D707 indexes had high levels of total contamination consistently >5% and as high as 13% (Fig. 3A). A more granular analysis of contamination that traced contamination sources on a per-tube level revealed that for the reaction indexed by D707/D504, nearly all of the contamination originated from the D701/D504 reaction. Conversely, the D701/D504 well was the main destination of contaminating EMIs that originated from the D707/D504 reaction (Fig. 3B). Similar cross-contamination patterns were observed in the other wells indexed by D701 and D707. These data suggested to us that our D701 and D707 indexes themselves had become cross-contaminated, perhaps during oligo synthesis or index preparation. We also observed that most of the remaining contamination occurred in wells that have a D7xx or D5xx index in common, which is consistent with contamination due to index misassignment (Fig. 3C). To address these issues, we performed library barcoding with dual unique indexes.32 Tru-seq HT style combinatorial indexing using D7xx/D5xx pairs resulted in a median contamination of 0.5% (maximum 8.9%). Dual unique indexes reduced the observed contamination to 0.006% (maximum 0.03%), a nearly 100-fold improvement (Figs. 3C and S3). In addition to contamination quantification, QCT analysis is also sensitive to sample mixups due to operator error during library preparation barcoding. If the same QCT fingerprint is observed in multiple samples at high read depth, this would

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indicate that a single assay was indexed to multiple barcodes. To quantify this, a sample collision score is deﬁned as the number of high read-depth EMI clusters shared between two PCRs. We measured the number of colliding EMI sequences across all pairs of PCRs in an experiment where 8 reactions were barcoded twice in the same sequencing run. When the pairwise collisions are plotted, barcodes 25-32 immediately stand out because they share ∼150 EMI clusters in common with corresponding barcodes 33-40. This approach can therefore be used to identify common operator errors that would result in a reduced number of ﬁngerprints in the sequencing data (Fig. 3D).

Single-gene NIPT enabled by single base pair molecular counting We next designed PCR assays for amplifying regions responsible for the most common genotypes of sickle cell disease, cystic fibrosis, spinal muscular atrophy (SMA), and alpha-thalassemia (Figs. 4 and S4). Sickle cell disease is most commonly caused by the rs334 (HbS) and rs33930165 (HbC) variants within exon 1 of HBB; over 90% of sickle cell disease cases are either HbSS or HbSC33. Since the paralogous genes HBB and HBD are highly similar in this region, >2bp of variation between HBB and HBD was included in the targeted region to ensure accurate read mapping (Fig. 4A). Cystic fibrosis is most commonly caused by recessive inheritance of rs113993960 (∆F508, Fig. 4B) in CFTR exon 11. Statistical modeling of the accuracy of NIPT results uses the pathogenic allele fraction (AF) observed from maternal blood, the number of DNA molecules assayed for the allele fraction measurement, and the fetal fraction measurement (Fig. 4C).34, 35 Since SMA and alpha-thalassemia are caused by deletions, modified designs were used to quantify the pathogenic allele fraction (Fig. S4). We first validated that accurate allele fraction measurements could be obtained from HBB exon 1. We generated ersatz cfDNA samples by shearing genomic sickle cell DNA to ∼150bp. Approximately 33ng total of sheared DNA was added to each PCR assay, and QCT analysis showed that 2,000 GE of HBB exon 1 were amplified from sheared DNA. In the ersatz samples of heterozygous sickle cell DNA (NA16266), we measured a mean HbS allele fraction of 0.500 +/- 0.0015 standard error of the mean (SEM) across 28 PCR replicates (Fig. 4D), indicating that HBB alleles are amplified without introducing any bias. The CV of the replicates was 1.3%-2.2% (95% confidence interval). Importantly, the CV is in accordance with the Poisson noise of 2.2% associated with 2000 molecules, suggesting that the assay for HBB allele fraction is operating at the physical limit of counting statistics. We next prepared a sheared DNA mixture of 90% heterozygous DNA and 10% homozygous sickle cell disease DNA to mimic cfDNA from a sickle cell carrier pregnancy with 10% SCD fetal fraction . The measured HbS allele fraction matched very closely with the expected HbS allele fraction of 0.55 (0.549-0.554, 95% CI; n=15). The large separation between the 0% and 10% SCD mixtures shows that pathogenic allele fraction differences for a sickle cell trait (heterozygous) vs sickle cell disease (homozygous) fetus can be easily discriminated. Similar results were obtained for >200 PCR replicates at mixtures ranging from 0% to 20% for HBB, cystic fibrosis, SMA, and alpha-thalassemia (Figs. 4E and S4). To confirm that sheared DNA behaves similarly to cell-free DNA, we next obtained 30 blood samples from male and female patients that are compound heterozygotes for sickle cell disease at Baylor College of Medicine (Fig. 5). cfDNA was purified from blood plasma and the HBB allele fraction assay was performed. The results of the HBB allele fraction assay again agreed 1 with the expected 2 allele fraction (Fig. 5A). On average, 3500GE of HBB were assayed for each 10mL blood sample. We measured 2.2% CV (1.6-2.9% 95% CI), which was in good agreement with the expected CV=1.7% associated with Poisson counting of 3500GE. The proportion of HBB DNA amenable to PCR amplification was 129GE/ng cfDNA (Fig. 5B), which is almost twice what we previously observed for sheared DNA (Fig. 2D). The increased numbers of assayable GE from cfDNA shows that the analytical validation performed with ersatz samples was more challenging than for cfDNA blood samples. This difference in capture efficiency could be because acoustically sheared DNA ( 150bp) was more fragmented than cfDNA (165bp) and/or differences between acoustic shearing and biological mechanisms of cfDNA fragmentation.29 Since none of the blood samples were taken from pregnancies, they can also serve as negative controls for NIPT analysis. None of the allele fraction measurements in these non-pregnant controls would have resulted in positive NIPT results (Fig. 5A).

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Clinical validation of single-gene NIPT We next obtained maternal blood and saliva samples from 208 healthy pregnant donors from Yashoda Hospital, Ghaziabad, India. Ethical clearance was obtained from the Yashoda Institutional Ethics Committee (IEC: ECR/970/Inst/UP/2017). While beta-thalassemia is common in India with a carrier rate as high as 5%, the distribution of pathogenic alleles is heterogeneous across subpopulations and geographic areas36. However, when we genotyped these samples, there were only 3 pregnant donors who were beta-thalassemia carriers. Therefore, to clinically validate HBB NIPT by molecular counting, we performed NIPT analysis for a linked, benign variant that is located within the target region of HBB exon 1 (Fig. 4A). Since HBB NIPT is an amplicon-based NGS assay, the ability to detect fetal inheritance of this variant (rs713040) is identical to that of SCD or any other pathogenic variants that are found in the target region of exon 1 (e.g., HbS, HbC, HbE, IVS1,1, IVS1,5). Moreover, because this variant has an extremely high minor allele frequency (0.2), we were able to obtain all maternal-fetal genotype combinations that represent healthy and disease states. Follow-up HBB genotyping of newborns was obtained for 52 pregnancies. However, the amount of cfDNA recovered from corresponding maternal plasma samples using the HBB allele fraction assay was found to be abnormally low (mean GE=996, min GE=21) compared to the cfDNA purified from negative controls collected at Baylor College of Medicine (mean GE=3500, min GE=1200; Fig. 5). Fifteen of the pregnant cfDNA samples contained <200 GE of assayed HBB. Since 200 GE corresponds to <2ng of cfDNA, these 15 samples were excluded from further analysis. Previous reports have shown that ∼4000GE should be recoverable from a 10mL blood sample.34, 37 The paucity of cfDNA recovered from these samples may be explained by incomplete filling of blood tubes, sample loss during cell-free DNA purification, or DNA degradation during extended storage at -20C (5-8 months). Our approach for NIPT integrates measurements of (i) fraction of fetal DNA present in cfDNA, (ii) molecular counts of assayed cfDNA, (iii) allele fraction of the maternal variant, and (iv) allele fraction of any variants that are not present in the maternal genotype (distinct paternally inherited variants). Approximately 1/3 of each purified cfDNA sample was used to determine the fetal fraction using a custom amplicon NGS assay that interrogates 86 common SNVs across all autosomal chromosomes (Fig. S5). QCT molecules were added to the remaining cfDNA and the HBB allele fraction assay was performed to determine molecular counts and allele fractions of HBB exon 1 variants. These measurements were then used in a statistical model to determine the genotype of the fetus. Because a fetus inherits one allele from each parent, in cases where the maternal genotype is homozygous, the detection of any non-maternal allele indicates that the fetus is heterozygous for that variant, i.e., inherited a paternal allele (paternal inheritance). On the other hand, for a sample where the maternal genotype is heterozygous, NIPT for recessive inheritance requires precise quantification of allele fraction to determine whether fetal alleles are contributing to an observed allele fraction 1 significantly different from 2 . For example, the expected variant allele fraction (VAF) in cfDNA for a heterozygous fetus will 1 remain at 2 , the same as the maternal level. However, if the fetus is homozygous for the variant, i.e., the fetus has inherited two identical alleles with the pathogenic variant, the VAF from the cfDNA sample should increase to 0.55 for a sample with 10% fetal fraction, to 0.60 VAF for 20% fetal fraction, and so on (recessive inheritance). The probability distributions of these allele fractions depends on the molecular counts due to Poisson counting noise (Figure 4C). In samples that had a homozygous (C/C reference or T/T variant) maternal genotype for the benign variant rs713040, we performed HBB NIPT by detecting a distinct paternally inherited fetal allele (n=14; Table 1). In these pregnancies, a heterozygous fetal genotype can occur only when the fetus inherits a paternal allele different from the maternal allele, and the 1 resultant minor allele fraction (MAF) in cfDNA is expected to be 2 of the fetal fraction (e.g. MAF of 0.05 when fetal fraction is 10%). Likewise, a homozygous fetus matching the genotype of the mother is determined by the absence of a paternal allele. This detection requires distinguishing a true, rare variant, i.e., the paternal allele, from any other sources of low-frequency variants introduced either by contamination, index misassignment, amplification, and/or sequencing errors. For these samples, QCT analysis indicated sample contamination and index misassignment was negligible (median=0.01%, max=0.2%). Then, we used a statistical model to ensure any observed minor alleles could not be explained by sequencing/polymerase error (Fig. 6A-B;

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discussed below). Since QCT analysis showed that these samples were sequenced to >50 reads/molecule, the contribution of read depth to noise is negligible, and sampling noise is nearly entirely due to the number of DNA molecules assayed. To rule out the possibility that the observed minor allele molecules are present due to sequencing error, we calculated two probability distributions for each sample: one for background sequencing error and the second for a paternally inherited allele. The fetal genotype is then determined by which probability distribution more closely matches the observed result. Hypothetically, the measured allele fraction could occur where the probability distributions have overlap. The likelihood ratio (LR) roughly corresponds to how likely it is for the observed measurement to be positive or negative, with LR > 1 indicating increased evidence for a positive result (affected fetus). To avoid calling NIPT results on inconclusive evidence, a no-call threshold was 1 34 set at 8 < LR < 8, a standard that has previously been used in single-gene NIPT . Examples of this analysis are shown in Figs. 6A and 6B for the cases of a homozygous and heterozygous fetus, respectively. The LR for all 14 paternal inheritance cases exceeded 10,000-fold, and HBB genotyping of newborns resulting from these pregnancies conﬁrmed that all of the NIPT calls were correct. This was true even in highly challenging samples with limited fetal fraction (<5%) or quantity of cfDNA available (<250GE). Out of the 14 maternal blood samples analyzed for paternal inheritance, 2 were homozygous negative C/C, 2 were heterozygous negative C/T, and 10 were homozygous positive T/T (Table 1). NIPT for recessive inheritance of HBB was then performed on samples with the heterozygous rs713040 maternal genotype, T/C. In these samples, the fetal genotype could be (i) homozygous reference allele, T/T; (ii) heterozygous T/C; or, (iii) homozygous for variant allele C/C. To more closely match the clinical use case of determining fetal disease status for a recessive disorder, the T/T and T/C genotypes were considered ‘normal,’ while the C/C was considered ‘affected.’ As in paternal inheritance NIPT, recessive inheritance NIPT uses a statistical model to compute the likelihood ratio of an affected vs. normal fetal genotype. Out of the 13 samples in which NIPT results and newborn follow-up were available, 5 were C/C affected, 3 were T/T normal, and 5 were T/C normal (Table 2, Fig. 6C-E). All NIPT results agreed with follow-up neonatal genotyping. Recessive inheritance NIPT analysis was performed on an additional 10 cfDNA samples, but the results were indeterminate based on the LR thresholds (Table S1). Notably, even lower amounts of cfDNA were recovered from the no-call samples (mean GE=600) compared to the samples for which recessively inherited NIPT calls were made (mean GE=1800). The strength of incorporating molecular counting into NIPT is that unreliable samples can be identiﬁed and incorrect calls can be avoided. Given that cfDNA obtained from HBB heterozygotes at Baylor College of Medicine had mean cfDNA molecular counts of 3500GE (Fig. 5), we expect the no-call rate to not be an important issue in a clinical setting. Overall, 27/27 NIPT calls, including paternal and recessive inheritance, were concordant with the newborn HBB genotype.

Discussion

There is significant unmet medical need for NIPT of these recessively inherited single-gene disorders. Affected individuals face significantly reduced life-span and require frequent access to intensive medical care. Current medical guidelines set by the American College of Obstetrician & Gynecologists and American College of Medical Genetics recommend that all pregnancies should be screened to determine the carrier status of the parents before offering genetic counseling and invasive diagnostic procedures.21 Moreover, reducing the need for paternal DNA could be a significant improvement in clinical practice. Current screens that test the mother for carrier status result in 11% to 30% positive calls and require follow-up with paternal carrier testing.38 However, paternal carrier testing may be unavailable or unreliable. Paternity is not established at the time of birth in 30% of US births to single-mothers,39 and this is in addition to >0.8% hidden non-paternity that has been uncovered in population-level genetic studies.40 Single-gene NIPTs require precision at the level of molecular counts. Digital PCR is an alternative method for obtaining molecular count data.19, 20 In fact, NIPT for sickle cell disease and beta-thalassemia has previously been demonstrated in proof-of-principle studies using dPCR.41, 42 However, dPCR has two critical limitations that hinder its translation to the clinic. First, dPCR relies on ∼15-30nt SNV-specific probes and therefore does not the have the single-base pair resolution of a sequencing approach. If nearby cis-variants are present within this footprint or if a high homology region is also amplified, a

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significant proportion of cases can be missed by dPCR. For instance, this is particularly acute in sickle cell disease, where HbC, HbS, and rs713040 are all within 12bp of each other; and where the HBD gene is a close paralog of HBB. These factors might have contributed to the 80% accuracy obtained in a previous study that used dPCR for NIPT of sickle cell disease.41 In addition, the multiplexability of digital PCR is limited to only 2-4 variants. This significantly limits its throughput and use for multiple variants, disorders, or samples at the same time, and has become a crucial impediment to its widespread clinical use. To overcome these limitations, we developed a method for counting DNA molecules using amplicon-based next-generation DNA sequencing and QCT analysis. The QCT molecular counting approach uses synthetic DNA that co-amplifies with the gene of interest (e.g. HBB, CFTR, etc.) to serve as a reference standard. The exact number of QCT molecules added to each sample is reconstructed from sequencing data which then enables molecular count information of the gene of interest to be recovered from sequencing depth data. Therefore, the synthetic QCT DNA used as a reference standard is calibration free and not susceptible to the Poisson noise associated with addition of ∼100-400 synthetic DNA molecules. Furthermore, since QCT molecular counting is compatible with amplicon sequencing, multiple loci can be simultaneously interrogated via multiplex PCR, and as many as 50-100 sgNIPT assays can be pooled and sequenced on a single Miseq lane (200,000-400,000 reads per assay). Single-gene NIPTs using QCTs were developed for sickle cell disease, cystic fibrosis, beta-thalassemia, alpha-thalassemia, and spinal muscular atrophy. HBB NIPT was performed on blood taken from pregnant women, and results were confirmed by follow-up DNA sequencing of newborns, resulting in 100% concordant NIPT calls. Molecular counting ability enables statistical modeling of NIPT results so that false calls can be avoided with even the most challenging samples. In addition to improvements in single-base pair resolution and multiplexability, QCT molecular counting has 25-100x more dynamic range than digital PCR. Droplet digital PCR is most precise at measuring ∼20,000 molecules, with an upper limit of 100,000 molecules.19 Even low-throughput NGS instruments such as the Illumina MiSeq produce 25 million reads, which can be used to count 2.5 million molecules using the QCT method without any decrease in accuracy. This increase in dynamic range unlocks absolute quantification of DNA and RNA levels in a diverse range of applications including T-cell receptor profiling, transcriptomics, and microbiome applications. Prior to QCT molecular counting, diverse adapter/primer pools called UMIs have been used for detection of unique molecules as well as low resolution molecular counting.17, 43 QCTs rely on a similar computational approach in which sequence diversity is used to identify single molecules. However, the QCT approach embeds sequence diversity within the target region instead of requiring a ligation or pre-amplification step inherent to UMIs. This has several advantages, including (i) it avoids inefficient ligation and pre-amplification PCR steps that can result in >90% loss of the sample, resulting in lower sensitivity; and (ii) sequence diversity is contained within ∼100 molecules in the QCT approach, as opposed to the picomole (1011 molecules) amounts of UMIs that can lead to primer/adapter dimers and hinder multiplexing.44, 45 We also found that QCTs were useful for ensuring the integrity of sample preparation and sequencing in addition to molecular counting. We point out that these features should also enable more reliable and sensitive rare variant detection in liquid biopsy. Currently, the limit of detection (LOD) for circulating tumor DNA is typically given in terms of allele fraction. However, an LOD of 0.1% allele fraction is not meaningful when a particular sample only contains 500 haploid genomic equivalents (less than 1 molecule). The QCT molecular counting approach enables an LOD to be calculated as an absolute number of tumor DNA molecules. Furthermore, reliable rare-variant detection requires assurances that minor alleles are not the result of cross-contamination from previously processed or positive control samples. We have integrated QCT cross-contamination analysis into our workflow for detecting contamination from fluid handling, operator error, and index misassignment. Measurements of contamination and index misassignment in every sample, rather than only as a negative control, enable a lower limit of detection to be obtained in liquid biopsy applications. Since QCTs are internal amplification controls that are present in every sample, they are also sensitive to and can help identify common problems that can negatively impact assay performance, such as PCR inhibition from hemolysis or salt and ethanol carryover during DNA purification. Perhaps, the highest technical impact of QCT molecular counting may be on quantification of gene copy number variation (CNV). 5-10% of the human genome consists of CNV >50bp, and an additional 33% of the genome is susceptible to CNV in

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cancerous tissues.46, 47 While CNVs are often seen as hallmarks of cancer, current liquid biopsy approaches are limited in their capability for CNV detection. Our NIPT results, particularly those that require copy number variation detection such as spinal muscular atrophy and alpha-thalassemia, suggest that QCT molecular counting can detect a single additional copy of a gene at 5% allele fraction. This is a signiﬁcant improvement to current liquid biopsies that can only detect 6 additional copies or more of an oncogene, e.g., HER2, at tumor fractions of 5-20%.14, 15

Methods

QCT synthesis and analysis QCT pools were synthesized by IDT as 4nmol scale ultramers. Sequences for HBB Exon 1 QCTs are given in Table S2. QCT oligos were double stranded by addition of primer hbs_qct_pext and incubation with Klenow polymerase at 37C for 1 hour. Double stranded QCT pools were then gel-purified and diluted to 15fM in TE-Tween (10mM Tris, 0.1mM EDTA, 0.05% Tween-20). QCT reads matching the QCT identifier were extracted from raw sequencing reads. For each PCR reaction, EMI sequence clusters were generated by grouping EMI sequences together if they differ by 2 or fewer mismatches. EMI sequence clusters were then thresholded into high- or low- read depth clusters by read depth. The read depth threshold is computed for each PCR reaction as the square root of the mean EMI sequence cluster read depth. The number of QCT molecules in that PCR reaction is the number of high-depth EMI sequence clusters, and hDQCT i is the mean read depth per QCT molecule. Contaminating QCT reads are identified by a low-read depth EMI sequence cluster in one PCR assay that appears in another PCR assay at high-read depth. The EMI fingerprint for the PCR reaction is the set of high read depth EMI sequences. The assayed genomic DHBB equivalents of HBB exon 1 DNA is computed as , where DHBB is the read depth of the HBB Exon1 amplicon. hDQCT i

DNA Purification and Library Prep Approximately 10mL of venous blood was collected into a cfDNA blood collection tube (Streck, Omaha, NE). Plasma was separated from whole blood according to manufacturer instructions and stored at -20C until further processing. Cell-free DNA was purified from plasma via the Qiagen Circulating Nucleic Acid kit using an elution volume of 50ul. An 86-plex amplicon NGS assay was designed to measure paternal inheritance of common allele frequency SNVs (Fig. S5). The fetal fraction was determined as 2εˆ, where εˆ is the median MAF of paternally inherited alleles across at least 9 loci. 10ul of cfDNA was used per fetal fraction assay. To perform the HBB exon 1 allele fraction assay, approximately 200 HBB QCT molecules were added to 35ul cfDNA, and the mixture was PCR-amplified and sequenced on the Illumina Miseq using dual unique indexes.

Non-pregnant cell-free DNA controls Samples were obtained from 30 pediatric patients with SCD receiving care at Texas Children’s Hospital Hematology Center. Samples were collected from both genders, aged 2-18 years of age, obtained under a Baylor College of Medicine Internal Review Board approved protocol. Venous blood was collected in 10mL Streck cfDNA tubes, and he cfDNA was puriﬁed from plasma using the Qiagen circulating nucleic acids kit. The HBB allele fraction assay was performed on all samples. The remaining cfDNA in these samples were either used in the fetal fraction assay as negative controls or analyzed by ﬂuorometry (Qubit) to compare DNA mass and assayed genomic equivalents (Fig. 5B).

Pregnant maternal blood samples Buccal swabs and 6-10mL venous blood in Streck tubes were obtained from 208 pregnancies at >12 weeks of gestation at Yashoda Hospital, Ghaziabad, India. Ethical clearance for this study was obtained from the Institutional Ethics Committee of Yashoda Hosipital (IEC: ECR/970/Inst/UP/2017). Of these patients, we were able to obtain buccal swabs of 52 newborns for conﬁrmation of NIPT results. HBB genotyping of the newborns was performed by PCR ampliﬁcation of the HBB gene using primers HBBNextera500F1 and HBBNextera500R1 to generate a 2.5kb amplicon, followed by Nextera-based library

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preparation and Miseq sequencing. The GATK Hapolotype Caller was then used to call variants from sequencing data.48 Cell-free DNA was puriﬁed from maternal plasma by the Qiagen Circulating Nucleic Acids kit with an elution volume of 50ul. 10ul of cfDNA was used for measuring fetal fraction. 35ul of cfDNA was used for the HBB exon 1 allele fraction assay.

NIPT Statistical Modeling The binomial distribution was used to model the probability of measuring the HBB allele fraction, x; given fetal fraction, 2ε; N Nx N(1−x) and DNA molecule count, N. The likelihood of a paternal allele is then P(x|N,ε) = Nx ε (1 − ε) , and the probability N Nx N(1−x) of sequencing error is P(x|N,s) = Nx s (1 − s) . The rate of sequencing error, s, was set at 0.5%. The LR that the fetus ε Nx 1−ε N(1−x) inherited a paternal allele compared to sequencing error then the ratio of these two probabilities, LR = ( s ) ( 1−s ) . For N 1 Nx 1 N(1−x) maternal inheritance, we computed likelihoods for an affected vs heterozygous fetus, pa f f = Nx ( 2 + ε) ( 2 − ε) and N N pa f f phet = 1/2 , and samples that had likelihood ratio 1/8 < < 8 where considered no-calls. Fetal fraction of HBB exon Nx phet 1 was adjusted by a factor of 0.74 to match levels observed in pregnant samples without follow-up (Fig. S4).

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Acknowledgements We gratefully acknowledge Vimal Jain, Mukesh Bhardwaj, Dr. Amit Bhalla, Dr. Sanjay Srivastava, Dr. Pradeep Varshney, Dr. Shilpa Chapadgaonka for their assistance with sample collection at Yashoda Hospital and for providing laboratory facilities at

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Manav Rachna International University. Research reported in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number R43HL144322. 10% was funded by federal sources. 90% was funded by BillionToOne. The content is solely the responsibility of the authors and does not necessarily represent the ofﬁcial views of the National Institutes of Health.

Author contributions statement DST, SS, BPL, VAS, and OA conceived and designed experiments. DST, SS, BPL, AN, RS, NI, CKK, and OA conducted experiments. DST, BPL, and OA analyzed the results. DST and OA wrote the manuscript. All authors reviewed the manuscript.

Additional information DST, SS, BPL, AN, and OA are or were employees of BillionToOne and hold stock or options to hold stock in the company. Patent disclosures about this work have been ﬁled with BillionToOne, Inc. BillionToOne, Inc. funded portions of this study.

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B 8

Figure 1. Overview of single-gene NIPT. (A) Clinical workﬂow uses a single blood tube of maternal blood. Amplicon-based assays are performed and sequenced using an Illumina Miseq instrument. (B) Bioinformatic analyses recover the dosage of pathogenic alleles, the fraction of cfDNA isolated from maternal blood that is of fetal origin, the number of DNA molecules assayed, and paternal inheritance of variants not found in the mother’s genotype. These analyses are combined with the maternal genotype to perform a statistical analysis of fetal genotype, resulting in an NIPT report. 7

Figure 1: Overview of single-gene NIPT. (A) Clinical workﬂow uses a single blood tube of maternal blood. Amplicon-based assays are performed and sequenced using an Illumina Miseq instrument. (B) Bioinformatic analyses recover the dosage of pathogenic alleles, the

fraction of cfDNA isolated from maternal blood that is of fetal origin, the number of DNA 6 molecules assayed, and paternal inheritance of variants not found in the mother’s genotype. These analyses are combined with the maternal genotype to perform a statistical analysis of fetal genotype, resulting in an NIPT report. 5

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A B

for. primer rev. primer EMI1 GOI amp seq 5’ 3’ GOI EMI2 EMI3 GOI 5’ N10 CTAGT 3’ QCT { { EMI Clustering Molecule Counting Embedded QCT ID EMI1: 4 reads reads Molecular < DQCT > = 3.0 Identiﬁer EMI2: 3 reads molecule EMI3: 2 reads 6 GOI reads ⟹ 2.0 GOI molecules

C D E 8

160 qct1 qct2 ● ● ***

> ● 300 2200 I 120 ● M ● ● ● E 2100

● ● D ● ● 200 ● ●

● 2000 < 80 ● ● 7 ● ● ● ●●● ● ●● 2 ●● 1900 ●●● ● ● ● ●●● QCT2 ● ● ● 100 ● ● ● # EMI Clusters #EMI 40 ●● ● # EMI clusters ● 1800 ● ● ●● QCT ●●● ● ● ● ● Genomic equivalents ● ● 0 30 33 40 80 120 160 0 1 2 4 0 1 2 4 QCT1 DNA mass (ng) RelativeDilution QCT factor Spike-in QCT1 < DEMI > 6

Figure 2. Accurate measurements of assayed genomic equivalents by Quantitative Counting Template (QCT) molecules. (A) Sequence design of QCT molecule pools. QCTs are designed to co-amplify with a gene of interest (GOI). An Embedded Molecular IdentifierFigure comprised2: Accurate of measurements randomized bases of ensures assayed each genomic QCT molecule equivalents has a by unique Quality sequence. Control (B) Schematic of QCT moleculeTemplate counting (QCT) analysis. molecules. QCT molecules (A) Sequence are added design to the DNAof QCT sample, molecule and the pools. mixture QCTs is amplifiedare and sequenced. EMI reads maydesigned have sequencing to co-amplify errors (graywith a bars), gene which of interest are identified while incorporating and corrected fora highly by clustering diverse analysis.Embedded The number of 5 GOI moleculesMolecular in the sample Identifier is calculated via randomized from the GOI bases read that depth ensures and the each average QCT number molecule of reads has per a unique EMI cluster. (C) Pools QCT1 and QCT2sequence. were synthesized (B) The number with different of EMI QCT sequence IDs and clusters diluted is to equal approximately to the number 100 molecules of QCT at 1x dilution. The barplot showsmolecules. the mean across 24 PCR replicates at each dilution factor. Error bar is 1 standard error of the mean. (D) The average depth per QCT1 molecule and QCT2 molecule in a PCR tube is shown for the 48 PCR replicates at dilution factors 1x 4 and 2x. Dashed line has slope=1, intercept=0. (E) 30ng or 33ng of sheared genomic DNA was added to >100 QCTs, amplified, and sequenced.E. At 30ng 30ng, or the 33ng assayable of sheared sheared genomic DNA mean DNA was was 1849 added molecules to >100 (CV QCTs. = 5.2% for, n=8); at 33ng the mean was 2066 molecules (CV=4.4%,30ng, obtained n=8). The mean mean 1849 GE measured molecules for 33ng(CV = was 5.2%, significantly n=8); 33ng greater got 2066 that that for 30ng (p = 0.00019, one sided t-test). molecules (CV=4.4%, n=8). The mean GE measured for 33ng was significantly greater that that for 30ng (p = 0.00019, one sided t-test). Since the overall CV = Poisson + QCT + pipet, the QCT error could be 3 less. 2

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D507 0.03 0.01 0.00 0.02 0.02 D5010.01 0.030.120.020.010.020.03 0.010.940.010.940.010.89 0.13 0.01 0.02 0.95 0.90 0.92

D506 0.10 0.04 0.03 0.01 0.01 0.01 D7010.04D7020.01D7030.01 D7040.99D7050.79D7060.93 D707 D708 D709 D710 D711 D712 i7 indexQCT Contamination D505 0.05 0.01 0.02 0.02 0.02 0.02 0.08 0.01 0.02 0.91 0.92 0.82 Fraction

D504 0.04 0.01 0.01 0.00 0.00 0.00 0.07 0.00 QCT0.00 Contamination0.88 0.95 0.92Fraction i5 index 0.00 0.05 0.10 D503 0.05 0.02 0.01 0.01 0.01 0.02 0.08 0.01 0.01 0.88 0.91 0.92

D502 0.05 0.02 0.04 0.03 0.02 0.02 0.13 0.04 0.03 0.94 0.85 0.91

D501 0.03 0.02 0.02 0.01 0.01 0.01 0.13 0.01 0.02 0.95 0.90 0.92 8 D701 D702 D703 D704 D705 D706 D707 D708 D709 D710 D711 D712 B i7 index

Source of D707/D504 Num.Contam. reads reads Dest of D707/D504 Num.Contam. reads reads

D508 D508 7 D507 2000 D507 1500 D506 D506 D505 1500 D505 D504 X D504 O 1000 48

i5 index D503 1000i5 index D503 47 D502 D502 500 46 D501 500 D501 45 6 44 D701 D702 D703 D704 D705 D706 D707 D708 D709 D710 D711 D712 D701 D702 D703 D704 D705 D706 D707 D708 D709 D710 D711 D712 43 i7 index i7 index 42 41 40 39 38 5 37 C D 36 D712_D508 D712_D507 D712_D506 D712_D505 D712_D504 35 D712_D503 48 D712_D502 D712_D501 47 D711_D508 D711_D507 D711_D506 46 D711_D505 34 D711_D504 D711_D503 45 D711_D502 D711_D501 44 D710_D508 D710_D507 D710_D506 43 33 D710_D505 D710_D504 D710_D503 42 D710_D502 D710_D501 41 D709_D508 Contam. D709_D507 32 D709_D506 40 D709_D505 D709_D504 39 D709_D503 D709_D502 D709_D501 frac_readsFraction 38 D708_D508 D708_D507 31 37 4 D708_D506 D708_D505 D708_D504 2a 36 D708_D503 num_collisions D708_D502 D708_D501 35 D707_D508 30 D707_D507 D707_D506 34 D707_D505 D707_D504 33 D707_D503 D707_D502 D707_D501 32 29 D706_D508 D706_D507 D706_D506 31 D706_D505 1e−02 num_collisions D706_D504 30 D706_D503 D706_D502 28 D706_D501 29 D705_D508 3 D705_D507 28 D705_D506 D705_D505 100 100 D705_D504 27 D705_D503 27 D705_D502 D705_D501 26 D704_D508 1e−03 D704_D507 25 D704_D506 D704_D505 D704_D504 24 26 D704_D503 D704_D502 Barcode D704_D501 23 10 D703_D508 D703_D507 22 D703_D506

D703_D505 Index 25 D703_D504 21 D703_D503 D703_D502 1e−04 20 D703_D501 D702_D508 D702_D507 19 D702_D506 D702_D505 1 3 24 Sample_ID.src D702_D504 2b 18 D702_D503 Contam.Source D702_D502 17 D702_D501

Barcode D701_D508 D701_D507 16 D701_D506 23 D701_D505 D701_D504 1e−05 15 10 D701_D503 D701_D502 14 D701_D501 1 BTO_024 BTO_023 13 22 BTO_022 BTO_021 BTO_020 12 BTO_019 BTO_018 11 BTO_017 BTO_016 21 BTO_015 10 BTO_014 BTO_013 BTO_012 9 BTO_011 BTO_010 8 BTO_009 20 BTO_008 BTO_007 7 BTO_006 BTO_005 BTO_004 6 BTO_003 BTO_002 5 19 BTO_001 4 3

Num.Collisions 1 18 2

BTO_001 BTO_002 BTO_003 BTO_004 BTO_005 BTO_006 BTO_007 BTO_008 BTO_009 BTO_010 BTO_011 BTO_012 BTO_013 BTO_014 BTO_015 BTO_016 BTO_017 BTO_018 BTO_019 BTO_020 BTO_021 BTO_022 BTO_023 BTO_024 1 D701_D501 D701_D502 D701_D503 D701_D504 D701_D505 D701_D506 D701_D507 D701_D508 D702_D501 D702_D502 D702_D503 D702_D504 D702_D505 D702_D506 D702_D507 D702_D508 D703_D501 D703_D502 D703_D503 D703_D504 D703_D505 D703_D506 D703_D507 D703_D508 D704_D501 D704_D502 D704_D503 D704_D504 D704_D505 D704_D506 D704_D507 D704_D508 D705_D501 D705_D502 D705_D503 D705_D504 D705_D505 D705_D506 D705_D507 D705_D508 D706_D501 D706_D502 D706_D503 D706_D504 D706_D505 D706_D506 D706_D507 D706_D508 D707_D501 D707_D502 D707_D503 D707_D504 D707_D505 D707_D506 D707_D507 D707_D508 D708_D501 D708_D502 D708_D503 D708_D504 D708_D505 D708_D506 D708_D507 D708_D508 D709_D501 D709_D502 D709_D503 D709_D504 D709_D505 D709_D506 D709_D507 D709_D508 D710_D501 D710_D502 D710_D503 D710_D504 D710_D505 D710_D506 D710_D507 D710_D508 D711_D501 D711_D502 D711_D503 D711_D504 D711_D505 D711_D506 D711_D507 D711_D508 D712_D501 D712_D502 D712_D503 D712_D504 D712_D505 D712_D506 D712_D507 D712_D508 17 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 2 Sample_ID.dest Barcode 16 Contam. Destination Index 15 14 13 12Figure 3. QCT tracking analysis quantiﬁes contamination and sample mixups. Either 4x, 2x, 1x or 0 QCT molecules were 11 10

added to 96 PCR reactions and sequenced on an Illumina Miseq lane. Within each PCR reaction, EMI sequence clusters were1in 9classified as contamination based on a read depth threshold (see Methods and Fig. S3). (A) The QCT contamination fraction 8 7for a given PCR is the proportion of QCT reads in that PCR well that are due to contamination from another PCR well. Wells 6 8 7 6 5 4 3 2 1in 5are shaded and annotated as the fraction of its reads that are due to contamination. (B) Contamination can further be traced to 4specific PCR wells. Contaminating EMI clusters found in D707/D504 (yellow box, A) are traced to high-depth EMI clusters 3 2found elsewhere in the experiment (left), and the high-depth EMI clusters of D707/D504 are also found as contaminating reads 1in other wells (right). (C) Dual-unique indexes drastically reduce index misassignment. Both dual-unique indexes and Truseq-style1 2 3 4 5 6 barcoding7 8 9 10 11 were12 13 14 used15 16 17 on18 12019 20 samples21 22 23 24 25 prepared26 27 28 29 and30 31 32 sequenced33 34 35 36 37 in38 batch.39 40 41 42 Contamination43 44 45 46 47 48 source/destination is shown Barcode for all pairs of the 120 PCR reactions. The reactions that used unique dual indexes (1) had nearly 100-fold less contamination compared to Truseq-style barcoding. Cross-contamination of D701 and D707 is found at rates exceeding 1% (2a and 2b). Index misassignment between D5xx barcodes are also evident (3). (D) Pairwise analysis of QCT fingerprints identifies sample mixups. Forty-eight PCR reactions were processed in parallel, dual unique indexed, and sequenced on a Miseq lane. The similarity of QCT fingerprints is quantified as the number of high read depth EMI clusters in common (i.e. collisions) between two reactions. The number of collisions for all pairs of PCR reactions is shown. 16/20 bioRxiv preprint doi: https://doi.org/10.1101/597732; this version posted April 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

A rs33930165 rs33915217

rs713040 rs334 rs33986703 rs33950507 M rs33971440

HBB ATGGTGCATCTGACTCCTGAGGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGCAGGTTGGTAT ||||||||||||||||||||||||||| |||| || | ||||||||||||||| ||||||||||||| |||||||||||||||||||||||||||||||| HBD ATGGTGCATCTGACTCCTGAGGAGAAGACTGCTGTCAATGCCCTGTGGGGCAAAGTGAACGTGGATGCAGTTGGTGGTGAGGCCCTGGGCAGGTTGGTAT

rs121908745 rs77646904 8 CFTR TTAAAGAAAATATCATCTTTGGTGTTTCCTATGATGAATATAGATACAGAAGCGTCATCAAAGCATGC

rs113993960 7 C D

0.5 AF =0.5 0.500.525 AF =0.550.55 0.55 (normal) (affected) 5000 5000

●

log2(LR) 6 4000 log2(lr_clip)log2(lr_clip) 4000 ● ● 15 ● ● 15 0.55 ● ● 10 ● 3000 ● 3000 10 ● 3 3 HbS AF 2000 −3 ● ● ● ● 2000 −3 ● ● ● ● ● ● 5 −10 ● 0.50 ● ● ● 1000 −10 ● −15 ●

Genomic Equivalents ● 1000 −15 ● Genomic Equivalents 0% 10% 0 HbS Spike−in 0 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 0.05 0.10Fetal0.15 Fraction0.20 0.05 0.10 0.15 0.20 Fetal Fraction 4

E 3

0.60

0.55 2 Fdel508 AF F508 AF F508∆

0.50

0% 10% 20% ∆F508Fetal FractionSpike-in 1in

8 7 6 5 4 3 2 1in Figure 4. NIPT for sickle cell disease and cystic fibrosis by amplicon NGS. (A) Target region of HBB exon 1 NIPT and alignment with HBD. Start codon is shown as ‘M’. rs713040 is a benign variant with high allele frequency. rs33930165 and rs334 are pathogenic variants. (B) Target region for CFTR NIPT of the ∆F508 variant. (C) The NIPT assay uses pathogenic allele fraction, fetal fraction, and assayed genomic equivalents data to compute a likelihood ratio (LR) for an affected 1 homozygous vs heterozygous fetus. Regions where 8 < LR < 8 are shaded white to represent no-call regions. Affected NIPT calls are shaded blue, and non-affected calls are shaded red. (D) Validation of allele fraction measurement for sickle cell disease. Genomic DNA corresponding to a heterozygous sickle cell (NA16266) mother was sheared to 150bp and 0 to 10% sheared sickle cell disease DNA (NA16265) was mixed in. The HbS allele fraction (AF) was measured by amplicon NGS sequencing. When no SCD DNA is mixed in, the mean HbS AF = 0.500 (n=28, CV=1.6%). With 10% SCD DNA, mean HbS AF = 0.551 (n=15, cv=1.7%). QCT analysis measured 2000GE in these assays. (E) Validation of allele fraction measurement for cystic fibrosis. Sheared homozygous cystic fibrosis variant ∆F508 was spiked-in to 30ng heterozygous ∆F508 sheared DNA at the given proportions.

17/20 bioRxiv preprint doi: https://doi.org/10.1101/597732; this version posted April 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

negative positive 60

density 20

0 0.50 0.55 HbS Allele Fraction

B 8 y = 129x + 36, r2 = 0.82

6000 ● ●

● ● 4000 ● 7 ● ●

GE ●

● ●

2000 ● ● ●

0 6 10 20 30 40 cfDNAcfDNA concentration mass (ng) (ng/ul) 5 Figure 5. SCD NIPT on negative control cfDNA resulted in no false-positives. Optimized HBB probes were validated for use on cfDNA by performing the NIPT assay on 10mL of venous blood from non-pregnant compound heterozygotes (HbAS or Figure 5: SCD NIPT on negative control cfDNA resulted in no false-positives. Optimized HbSC). The mean HbS AF was 0.498 withHBB a coefﬁcientprobes were validated of variation for use on ofcfDNA 2.2% by performing (n=30). the The NIPT mean assay on number 10mL of venous of HBB molecules

blood from non-pregnant compound heterozygotes (HbAS or HbSC). The mean HbS AF was 4 assayed in each blood tube was 3500. Our0.498 results with agree a coefficient with ofthe variation expected of 2.2% HbS (n=30). AF=1/2 The mean and number CV=1.7%; of HBB molecules the histogram of assayed in each blood tube was 3500. Our results agree with the expected HbS AF=1/2 and measured HbS AF corresponds very well withCV=1.7%; the the theoretical histogram of binomial measured HbSdistribution AF corresponds in blue very (AF=1/2, well with the n=3500). theoretical Assuming that positive cases have a 10% fetal fraction (redbinomial curve), distribution none in ofblue the (AF=1/2, negative n=3500). controls Assuming would that positive have cases been have called a 10% fetal as positive for fetal fraction (red curve), none of the negative controls would have been called as positive for fetal SCD. (B) Assayed genomic equivalents ofSCD.HBB (B)exon Assayed 1 genomic in cfDNA. equivalents The of concentrationHBB Exon1 in cfDNA. of The 13 concentration cfDNA samples of 13 cfDNA was quantiﬁed by

samples was quantified by Qubit to determine the mass of cfDNA used in the HBB assay. On 3 Qubit to determine the mass of cfDNA usedaverage, in the 1 ngHBB of cfDNAassay. resulted On in the average, capture of 1 129 ng haploid of cfDNA genomic equivalents resulted of in HBB. the capture of 129 haploid genomic equivalents of HBB.

128.7 GE/ng +/-17.4 GE/ng (p 2 =1.3E-5) , intercept was 36 GE (+/- 490, p =0.943) 1in

8 7 6 5 4 3 2 1in

18/20 bioRxiv preprint doi: https://doi.org/10.1101/597732; this version posted April 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

A Paternal 1/1 B MAF=0.002 FF=4.5% GE=2742 Paternal 0/1 MAF=0.033 FF=8.1% GE=572 0.25

0.09 0.20

0.15 0.06 0.10

Probability 0.03 Probability 0.05

0.00 0.00 0 20 40 60 80 0 10 20 30 40 50 Num. Molecules Num. Molecules C Recessive 0/0 VAF=0.478 FF=13.4% GE=2092

3000 log2(lr_clip)log2 LR 8 0.020

0.015 2000 −3

0.010 1000 −10 Probability

0.005 7 −15

0.000 Genomic Equivalents 0 0.45 0.50 0.55 0.60 0.65 0.10 0.15 0.20 0.25 Allele Fraction Fetal Fraction 6 D

Recessive 0/1 VAF=0.505 FF=22.5% GE=1386 2500 log2(lr_clip)log2 LR 0.020 2000 5 −3 0.015 1500

0.010 1000 −10 Probability 0.005 500

−15 4

0.000 Genomic Equivalents 0 0.45 0.50 0.55 0.60 0.65 0.1 0.2 0.3 0.4 Allele Fraction Fetal Fraction

E 3 Recessive 1/1 VAF=0.568 FF=11.2% GE=2328 log2(lr_clip)log2 LR 3000 0.015 15

0.010 2000 2 10

1000

Probability 0.005 3

0.000 Genomic Equivalents 0 0.45 0.50 0.55 0.60 0.05 0.10 0.15 0.20 Allele Fraction Fetal Fraction 1in

8 7 6 5 4 3 2 1in Figure 6. NIPT of rs713040 in HBB exon 1. (A) Probability distribution of sample 04B for paternal inheritance resulting in a NIPT homozygous call. (B) Probability distribution of sample 38C for paternal inheritance resulting in a NIPT heterozygous call. Black and white arrows indicate diagnostic measurements of allele fraction, GE, and fetal fraction. (C) Negative call for recessive NIPT resulting in a homozygous child. (D) Negative recessive NIPT call resulting in a heterozygous child. (E) Positive recessive NIPT call resulting in an ‘affected’ child.

19/20 bioRxiv preprint doi: https://doi.org/10.1101/597732; this version posted April 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Table 1. NIPT for paternal inheritance of rs713040

Sample Fetal Fraction VAF Num. Mol. LR NIPT Neonate GT 35A 0.088 0.004 1159 1.5e+09 0/0 0/0 07B 0.272 0.009 482 5.9e+15 0/0 0/0 38C 0.081 0.033 572 2.1e-09 0/1 0/1 49E 0.174 0.961 436 2.0e-08 0/1 0/1 38B 0.228 0.998 871 4.3e+27 1/1 1/1 52E 0.128 0.998 248 4.7e+04 1/1 1/1 17D 0.172 0.998 585 1.6e+13 1/1 1/1 18C 0.123 0.998 636 3.6e+09 1/1 1/1 52F 0.213 0.998 447 5.2e+13 1/1 1/1 04B 0.045 0.998 2742 7.1e+09 1/1 1/1 36C 0.144 0.998 755 1.4e+14 1/1 1/1 02A 0.289 0.998 1497 5.1e+64 1/1 1/1 40B 0.237 0.998 988 2.7e+33 1/1 1/1 37A 0.209 1.000 2900 1.4e+84 1/1 1/1

Table 2. NIPT for recessive inheritance of rs713040

Sample Fetal Fraction VAF Num. Mol. LR NIPT Neonate GT 30B 0.153 0.477 8616 1.0e-44 normal 0/0 31B 0.171 0.478 304 1.5e-02 normal 0/0 08A 0.134 0.478 2092 3.5e-09 normal 0/0 56C 0.325 0.482 663 7.2e-12 normal 0/1 39A 0.079 0.498 1868 2.3e-02 normal 0/1 63B 0.225 0.505 1386 2.4e-08 normal 0/1 47F 0.308 0.508 1319 6.2e-14 normal 0/1 09B 0.208 0.521 1601 1.3e-04 normal 0/1 38A 0.112 0.568 2328 7.9e+07 affected 1/1 25B 0.242 0.569 360 1.7e+01 affected 1/1 40E 0.184 0.570 573 2.1e+02 affected 1/1 50C 0.179 0.586 1085 3.4e+06 affected 1/1 17B 0.219 0.603 1611 1.8e+14 affected 1/1

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